from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-27 14:07:42.059037
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 27, Dec, 2020
Time: 14:07:47
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.1596
Nobs: 153.000 HQIC: -45.2181
Log likelihood: 1650.70 FPE: 1.11762e-20
AIC: -45.9422 Det(Omega_mle): 6.32160e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.452610 0.160963 2.812 0.005
L1.Burgenland 0.145316 0.082038 1.771 0.077
L1.Kärnten -0.236942 0.066013 -3.589 0.000
L1.Niederösterreich 0.118514 0.190887 0.621 0.535
L1.Oberösterreich 0.255344 0.162696 1.569 0.117
L1.Salzburg 0.172877 0.084582 2.044 0.041
L1.Steiermark 0.077515 0.116932 0.663 0.507
L1.Tirol 0.152977 0.078011 1.961 0.050
L1.Vorarlberg 0.004208 0.075569 0.056 0.956
L1.Wien -0.126070 0.157620 -0.800 0.424
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.517586 0.209434 2.471 0.013
L1.Burgenland 0.013155 0.106742 0.123 0.902
L1.Kärnten 0.361582 0.085891 4.210 0.000
L1.Niederösterreich 0.124783 0.248368 0.502 0.615
L1.Oberösterreich -0.189486 0.211688 -0.895 0.371
L1.Salzburg 0.192046 0.110052 1.745 0.081
L1.Steiermark 0.247746 0.152143 1.628 0.103
L1.Tirol 0.144606 0.101502 1.425 0.154
L1.Vorarlberg 0.183296 0.098325 1.864 0.062
L1.Wien -0.578465 0.205083 -2.821 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.292370 0.070033 4.175 0.000
L1.Burgenland 0.109991 0.035694 3.081 0.002
L1.Kärnten -0.026900 0.028721 -0.937 0.349
L1.Niederösterreich 0.069600 0.083053 0.838 0.402
L1.Oberösterreich 0.287208 0.070787 4.057 0.000
L1.Salzburg -0.004791 0.036801 -0.130 0.896
L1.Steiermark -0.021009 0.050875 -0.413 0.680
L1.Tirol 0.090100 0.033942 2.655 0.008
L1.Vorarlberg 0.130667 0.032879 3.974 0.000
L1.Wien 0.079245 0.068578 1.156 0.248
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.194859 0.081236 2.399 0.016
L1.Burgenland -0.006490 0.041404 -0.157 0.875
L1.Kärnten 0.021122 0.033316 0.634 0.526
L1.Niederösterreich 0.026762 0.096338 0.278 0.781
L1.Oberösterreich 0.406518 0.082111 4.951 0.000
L1.Salzburg 0.096777 0.042687 2.267 0.023
L1.Steiermark 0.182794 0.059014 3.097 0.002
L1.Tirol 0.034417 0.039371 0.874 0.382
L1.Vorarlberg 0.102745 0.038139 2.694 0.007
L1.Wien -0.061070 0.079549 -0.768 0.443
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.577034 0.169149 3.411 0.001
L1.Burgenland 0.076439 0.086211 0.887 0.375
L1.Kärnten 0.007524 0.069370 0.108 0.914
L1.Niederösterreich -0.038466 0.200595 -0.192 0.848
L1.Oberösterreich 0.149027 0.170970 0.872 0.383
L1.Salzburg 0.048403 0.088884 0.545 0.586
L1.Steiermark 0.118095 0.122878 0.961 0.337
L1.Tirol 0.216624 0.081979 2.642 0.008
L1.Vorarlberg 0.014749 0.079412 0.186 0.853
L1.Wien -0.153549 0.165636 -0.927 0.354
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.163132 0.118457 1.377 0.168
L1.Burgenland -0.023474 0.060374 -0.389 0.697
L1.Kärnten -0.015368 0.048580 -0.316 0.752
L1.Niederösterreich 0.172024 0.140478 1.225 0.221
L1.Oberösterreich 0.392678 0.119732 3.280 0.001
L1.Salzburg -0.028961 0.062246 -0.465 0.642
L1.Steiermark -0.048823 0.086053 -0.567 0.570
L1.Tirol 0.192468 0.057410 3.352 0.001
L1.Vorarlberg 0.042101 0.055613 0.757 0.449
L1.Wien 0.163005 0.115996 1.405 0.160
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.212169 0.147110 1.442 0.149
L1.Burgenland 0.078515 0.074978 1.047 0.295
L1.Kärnten -0.046542 0.060331 -0.771 0.440
L1.Niederösterreich -0.037141 0.174458 -0.213 0.831
L1.Oberösterreich -0.117687 0.148693 -0.791 0.429
L1.Salzburg 0.005560 0.077302 0.072 0.943
L1.Steiermark 0.388201 0.106868 3.633 0.000
L1.Tirol 0.521366 0.071297 7.313 0.000
L1.Vorarlberg 0.217628 0.069065 3.151 0.002
L1.Wien -0.220228 0.144054 -1.529 0.126
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.104289 0.172315 0.605 0.545
L1.Burgenland 0.026258 0.087824 0.299 0.765
L1.Kärnten -0.114899 0.070668 -1.626 0.104
L1.Niederösterreich 0.225374 0.204349 1.103 0.270
L1.Oberösterreich 0.002559 0.174170 0.015 0.988
L1.Salzburg 0.220645 0.090547 2.437 0.015
L1.Steiermark 0.142512 0.125178 1.138 0.255
L1.Tirol 0.096911 0.083513 1.160 0.246
L1.Vorarlberg 0.025789 0.080898 0.319 0.750
L1.Wien 0.280807 0.168736 1.664 0.096
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.577570 0.095351 6.057 0.000
L1.Burgenland -0.016569 0.048598 -0.341 0.733
L1.Kärnten 0.000487 0.039104 0.012 0.990
L1.Niederösterreich -0.009607 0.113077 -0.085 0.932
L1.Oberösterreich 0.278061 0.096377 2.885 0.004
L1.Salzburg 0.009166 0.050104 0.183 0.855
L1.Steiermark 0.000427 0.069267 0.006 0.995
L1.Tirol 0.079786 0.046212 1.727 0.084
L1.Vorarlberg 0.177851 0.044765 3.973 0.000
L1.Wien -0.092767 0.093370 -0.994 0.320
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.142269 -0.012724 0.200575 0.240981 0.052126 0.096556 -0.090622 0.161121
Kärnten 0.142269 1.000000 -0.010493 0.183839 0.133167 -0.147749 0.171498 0.029197 0.298275
Niederösterreich -0.012724 -0.010493 1.000000 0.252474 0.074071 0.195520 0.090096 0.029566 0.347075
Oberösterreich 0.200575 0.183839 0.252474 1.000000 0.273804 0.290491 0.090445 0.062857 0.095512
Salzburg 0.240981 0.133167 0.074071 0.273804 1.000000 0.143955 0.058264 0.072145 -0.034262
Steiermark 0.052126 -0.147749 0.195520 0.290491 0.143955 1.000000 0.095061 0.081122 -0.138673
Tirol 0.096556 0.171498 0.090096 0.090445 0.058264 0.095061 1.000000 0.137816 0.124835
Vorarlberg -0.090622 0.029197 0.029566 0.062857 0.072145 0.081122 0.137816 1.000000 0.094432
Wien 0.161121 0.298275 0.347075 0.095512 -0.034262 -0.138673 0.124835 0.094432 1.000000